@inproceedings{925c0d4df38f4cfe8f8f53fe7fafeb0c,
title = "ZERO-SHOT MULTI-FOCUS IMAGE FUSION",
abstract = "Multi-focus image fusion (MFIF) is an effective way to eliminate the out-of-focus blur generated in the imaging process. The difficulties in focus level estimation and the lack of real training set for supervised learning make MFIF remain a challenging task after decades of research. According to DIP [1], a neural network can capture the low-level statistics of a single image and can be used as a prior for solving many low-level problems. Based on this idea, we propose a novel architecture named IM-Net comprised of I-Net to model the deep prior of the fused image and M-Net to model the deep prior of the focus map. Without any large scale training set, our method achieves zero-shot learning through the extracted prior information. Experiments on extensively used dataset demonstrate the effectiveness of our approach.",
keywords = "Multi-focus image fusion, deep image prior, zero-shot learning",
author = "Xingyu Hu and Junjun Jiang and Xianming Liu and Jiayi Ma",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Multimedia and Expo, ICME 2021 ; Conference date: 05-07-2021 Through 09-07-2021",
year = "2021",
doi = "10.1109/ICME51207.2021.9428413",
language = "英语",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE International Conference on Multimedia and Expo, ICME 2021",
address = "美国",
}